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Abstract
Generated crop insurance rates depend critically on the distributional assumptions of the
underlying crop yield loss model. Using farm level corn yield data from 1972-2008, we revisit
the problem of examining in-sample goodness-of-fit measures across a set of flexible
parametric, semi-parametric, and non-parametric distributions. Simulations are also conducted
to investigate the out-of-sample efficiency properties of several competing distributions. The
results indicate that more parameterized distributional forms fit the data better in-sample due to
the fact that they have more parameters, but are generally less efficient out-of-sample–and in
some cases more biased–than more parsimonious forms which also fit the data adequately, such
as the Weibull. The results highlight the relative advantages of alternative distributions in terms
of the bias-efficiency tradeoff in both in- and out-of-sample frameworks.